Knowledge Mapping to Understand Corporate Value: Literature Review and Bibliometrics
Bibliographic record
Abstract
The purpose of this study is to summarize the research results on corporate value published from 2000 to 2022; show the research overview, hot trends, and topic evolution of this research field; provide new ideas for the mining of the research frontiers of corporate value and a summary of the change rules of research hotspots; and describe prospects for the evolution direction and path of future research. Combining the bibliometric research method with a literature review, the research results on corporate value were analyzed quantitatively by querying the WOS database from 2000 to 2022; the analysis tool was CiteSpace. This study has five findings. First, researchers are paying increasing attention to the study of corporate value, and most of the research results are obtained by independent authors. Second, Chinese research institutions rank among the top three in publication volume. However, their research results have had little impact, with Univ Penn and Peking Univ having the most significant impact. Third, the top three keywords that scholars pay attention to are performance, impact, and corporate governance. Keyword burst analysis, CSR, value reliability, and sustainability are the latest research frontiers. Fourth, evolutionary trends are divided into three stages: research on the influencing factors of corporate value, research on the impact of corporate behavior on corporate value, and research on the evaluation and growth of corporate value. Fifth, knowledge domains include corporate value research methods, the factors influencing corporate value, and corporate behavior. The aims of this study are to provide a new perspective for researchers to study corporate value, provide new ideas for enterprise managers to manage corporate value, and achieve the sustainable development of corporate value. At the same time, the scientific knowledge graph method is applied in corporate value research, adding a new research path for corporate value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.125 | 0.144 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".